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Prediction Of Crop Yields Across Four Climate Zones In Germany: An Artificial Neural Network Approach

Author

Listed:
  • Thomas Heinzow
  • Richard S.J. Tol

    () (Economic and Social Research Institute, Dublin)

Abstract

This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may later be used to construct regional time series of agricultural output under climate change, based on the highly resolved output of the global circulation models and regional models. Three 30-year combined historical data sets of rural district yields (oats, spring barley and silage maize), daily temperatures (mean, maximum, dewpoint) and precipitation were constructed. They were used with artificial neural network technology to investigate, simulate and predict historical time series of crop yields in four climate zones of Germany. Final neural networks, trained with data sets of three climate zones and tested against an independent northern zone, have high predictive power (0.83

Suggested Citation

  • Thomas Heinzow & Richard S.J. Tol, 2003. "Prediction Of Crop Yields Across Four Climate Zones In Germany: An Artificial Neural Network Approach," Working Papers FNU-34, Research unit Sustainability and Global Change, Hamburg University, revised Sep 2003.
  • Handle: RePEc:sgc:wpaper:34
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    File URL: http://www.fnu.zmaw.de/fileadmin/fnu-files/publication/working-papers/Working-Paper34.pdf
    File Function: First version, 2003
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    Citations

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    Cited by:

    1. Richard S.J. Tol, 2006. "Integrated Assessment Modelling," Working Papers FNU-102, Research unit Sustainability and Global Change, Hamburg University, revised May 2006.
    2. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    3. Safa, Majeed & Samarasinghe, Sandhya, 2013. "Modelling fuel consumption in wheat production using artificial neural networks," Energy, Elsevier, vol. 49(C), pages 337-343.
    4. Rehdanz, Katrin & Tol, Richard S.J. & Wetzel, Patrick, 2006. "Ocean carbon sinks and international climate policy," Energy Policy, Elsevier, vol. 34(18), pages 3516-3526, December.
    5. Hamilton, Jacqueline M., 2007. "Coastal landscape and the hedonic price of accommodation," Ecological Economics, Elsevier, vol. 62(3-4), pages 594-602, May.

    More about this item

    Keywords

    global change; agriculture; artificial neural networks; yield prediction;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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